Optical metrology model optimization based on goals

ABSTRACT

The optimization of an optical metrology model for use in measuring a wafer structure is evaluated. An optical metrology model having metrology model variables, which includes profile model parameters of a profile model, is developed. One or more goals for metrology model optimization are selected. One or more profile model parameters to be used in evaluating the one or more selected goals are selected. One or more metrology model variables to be set to fixed values are selected. One or more selected metrology model variables are set to fixed values. One or more termination criteria for the one or more selected goals are set. The optical metrology model is optimized using the fixed values for the one or more selected metrology model variables. Measurements for the one or more selected profile model parameters are obtained using the optimized optical metrology model. A determination is then made as to whether the one or more termination criteria are met by the obtained measurements.

BACKGROUND

1. Field

The present application relates to optical metrology, and moreparticularly to optical metrology model optimization.

2. Related Art

Optical metrology involves directing an incident beam at a structure,measuring the resulting diffracted beam, and analyzing the diffractedbeam to determine various characteristics, such as the profile of thestructure. In semiconductor manufacturing, optical metrology istypically used for quality assurance. For example, after fabricating aperiodic grating structure in proximity to a semiconductor chip on asemiconductor wafer, an optical metrology system is used to determinethe profile of the periodic grating. By determining the profile of theperiodic grating structure, the quality of the fabrication processutilized to form the periodic grating structure, and by extension thesemiconductor chip proximate the periodic grating structure, can beevaluated.

In optical metrology, an optical metrology model is typically developedto measure a structure. The optical metrology model can be expressedusing metrology model variables. In general, the greater the number ofmetrology model variables that are allowed to float in developing theoptical metrology model, the greater the accuracy of the measurementsobtained using the optical metrology model. However, increasing thenumber of metrology model variables allowed to float also increases theamount of time needed to develop the optical metrology model.Additionally, in some cases, allowing too many metrology model variablescan produce erroneous measurements.

SUMMARY

In one exemplary embodiment, the optimization of an optical metrologymodel for use in measuring a wafer structure is evaluated. An opticalmetrology model having metrology model variables, which includes profilemodel parameters of a profile model, is developed. One or more goals formetrology model optimization are selected. One or more profile modelparameters to be used in evaluating the one or more selected goals areselected. One or more metrology model variables to be set to fixedvalues are selected. One or more selected metrology model variables areset to fixed values. One or more termination criteria for the one ormore selected goals are set. The optical metrology model is optimizedusing the fixed values for the one or more selected metrology modelvariables. Measurements for the one or more selected profile modelparameters are obtained using the optimized optical metrology model. Adetermination is then made as to whether the one or more terminationcriteria are met by the obtained measurements.

DESCRIPTION OF DRAWING FIGURES

The present application can be best understood by reference to thefollowing description taken in conjunction with the accompanying drawingfigures, in which like parts may be referred to by like numerals:

FIG. 1 depicts an exemplary optical metrology system;

FIGS. 2A-2E depict various hypothetical profiles of a structure;

FIG. 3 is an exemplary process for optimizing an optical metrologymodel;

FIG. 4 is an exemplary block diagram of an exemplary system foroptimizing an optical metrology model;

FIG. 5 is another exemplary process for optimizing an optical metrologymodel; and

FIG. 6 is still another exemplary process for optimizing an opticalmetrology model.

DETAILED DESCRIPTION

The following description sets forth numerous specific configurations,parameters, and the like. It should be recognized, however, that suchdescription is not intended as a limitation on the scope of the presentinvention, but is instead provided as a description of exemplaryembodiments.

1. Optical Metrology

With reference to FIG. 1, an optical metrology system 100 can be used toexamine and analyze a structure. For example, optical metrology system100 can be used to determine the profile of a periodic grating 102formed on wafer 104. As described earlier, periodic grating 102 can beformed in test areas on wafer 104, such as adjacent to a device formedon wafer 104. Alternatively, periodic grating 102 can be formed in anarea of the device that does not interfere with the operation of thedevice or along scribe lines on wafer 104.

As depicted in FIG. 1, optical metrology system 100 can include aphotometric device with a source 106 and a detector 112. Periodicgrating 102 is illuminated by an incident beam 108 from source 106. Inthe present exemplary embodiment, incident beam 108 is directed ontoperiodic grating 102 at an angle of incidence θ_(i) with respect tonormal {right arrow over (n)} of periodic grating 102 and an azimuthangle Φ (i.e., the angle between the plane of incidence beam 108 and thedirection of the periodicity of periodic grating 102). Diffracted beam110 leaves at an angle of θ_(d) with respect to normal {right arrow over(n)} and is received by detector 112. Detector 112 converts thediffracted beam 110 into a measured diffraction signal.

To determine the profile of periodic grating 102, optical metrologysystem 100 includes a processing module 114 configured to receive themeasured diffraction signal and analyze the measured diffraction signal.As described below, the profile of periodic grating 102 can then bedetermined using a library-based process or a regression-based process.Additionally, other linear or non-linear profile extraction techniquesare contemplated.

2. Library-Based Process of Determining Profile of Structure

In a library-based process of determining the profile of a structure,the measured diffraction signal is compared to a library of simulateddiffraction signals. More specifically, each simulated diffractionsignal in the library is associated with a hypothetical profile of thestructure. When a match is made between the measured diffraction signaland one of the simulated diffraction signals in the library or when thedifference of the measured diffraction signal and one of the simulateddiffraction signals is within a preset or matching criterion, thehypothetical profile associated with the matching simulated diffractionsignal is presumed to represent the actual profile of the structure. Thematching simulated diffraction signal and/or hypothetical profile canthen be utilized to determine whether the structure has been fabricatedaccording to specifications.

Thus, with reference again to FIG. 1, in one exemplary embodiment, afterobtaining a measured diffraction signal, processing module 114 thencompares the measured diffraction signal to simulated diffractionsignals stored in a library 116. Each simulated diffraction signal inlibrary 116 can be associated with a hypothetical profile. Thus, when amatch is made between the measured diffraction signal and one of thesimulated diffraction signals in library 116, the hypothetical profileassociated with the matching simulated diffraction signal can bepresumed to represent the actual profile of periodic grating 102.

The set of hypothetical profiles stored in library 116 can be generatedby characterizing a hypothetical profile using a set of parameters, thenvarying the set of parameters to generate hypothetical profiles ofvarying shapes and dimensions. The process of characterizing a profileusing a set of parameters can be referred to as parameterizing.

For example, as depicted in FIG. 2A, assume that hypothetical profile200 can be characterized by parameters h1 and w1 that define its heightand width, respectively. As depicted in FIGS. 2B to 2E, additionalshapes and features of hypothetical profile 200 can be characterized byincreasing the number of parameters. For example, as depicted in FIG.2B, hypothetical profile 200 can be characterized by parameters h1, w1,and w2 that define its height, bottom width, and top width,respectively. Note that the width of hypothetical profile 200 can bereferred to as the critical dimension (CD). For example, in FIG. 2B,parameter w1 and w2 can be described as defining the bottom CD and topCD, respectively, of hypothetical profile 200.

As described above, the set of hypothetical profiles stored in library116 (FIG. 1) can be generated by varying the parameters thatcharacterize the hypothetical profile. For example, with reference toFIG. 2B, by varying parameters h1, w1, and w2, hypothetical profiles ofvarying shapes and dimensions can be generated. Note that one, two, orall three parameters can be varied relative to one another.

With reference again to FIG. 1, the number of hypothetical profiles andcorresponding simulated diffraction signals in the set of hypotheticalprofiles and simulated diffraction signals stored in library 116 (i.e.,the resolution and/or range of library 116) depends, in part, on therange over which the set of parameters and the increment at which theset of parameters are varied. In one exemplary embodiment, thehypothetical profiles and the simulated diffraction signals stored inlibrary 116 are generated prior to obtaining a measured diffractionsignal from an actual structure. Thus, the range and increment (i.e.,the range and resolution) used in generating library 116 can be selectedbased on familiarity with the fabrication process for a structure andwhat the range of variance is likely to be. The range and/or resolutionof library 116 can also be selected based on empirical measures, such asmeasurements using atomic force microscope (AFM), or a cross sectionscanning electron microscope (XSEM), a transmission electron microscope(TEM), and the like.

For a more detailed description of a library-based process, see U.S.patent application Ser. No. 09/907,488, titled GENERATION OF A LIBRARYOF PERIODIC GRATING DIFFRACTION SIGNALS, filed on Jul. 16, 2001, whichis incorporated herein by reference in its entirety.

3. Regression-Based Process of Determining Profile of Structure

In a regression-based process of determining the profile of a structure,the measured diffraction signal is compared to a simulated diffractionsignal (i.e., a trial diffraction signal). The simulated diffractionsignal is generated prior to the comparison using a set of parameters(i.e., trial parameters) for a hypothetical profile (i.e., ahypothetical profile). If the measured diffraction signal and thesimulated diffraction signal do not match or when the difference of themeasured diffraction signal and one of the simulated diffraction signalsis not within a preset or matching criterion, another simulateddiffraction signal is generated using another set of parameters foranother hypothetical profile, then the measured diffraction signal andthe newly generated simulated diffraction signal are compared. When themeasured diffraction signal and the simulated diffraction signal matchor when the difference of the measured diffraction signal and one of thesimulated diffraction signals is within a preset or matching criterion,the hypothetical profile associated with the matching simulateddiffraction signal is presumed to represent the actual profile of thestructure. The matching simulated diffraction signal and/or hypotheticalprofile can then be utilized to determine whether the structure has beenfabricated according to specifications.

Thus, with reference again to FIG. 1, in one exemplary embodiment,processing module 114 can generate a simulated diffraction signal for ahypothetical profile, and then compare the measured diffraction signalto the simulated diffraction signal. As described above, if the measureddiffraction signal and the simulated diffraction signal do not match orwhen the difference of the measured diffraction signal and one of thesimulated diffraction signals is not within a preset or matchingcriterion, then processing module 114 can iteratively generate anothersimulated diffraction signal for another hypothetical profile. In oneexemplary embodiment, the subsequently generated simulated diffractionsignal can be generated using an optimization algorithm, such as globaloptimization techniques, which includes simulated annealing, and localoptimization techniques, which includes steepest descent algorithm.

In one exemplary embodiment, the simulated diffraction signals andhypothetical profiles can be stored in a library 116 (i.e., a dynamiclibrary). The simulated diffraction signals and hypothetical profilesstored in library 116 can then be subsequently used in matching themeasured diffraction signal.

For a more detailed description of a regression-based process, see U.S.patent application Ser. No. 09/923,578, titled METHOD AND SYSTEM OFDYNAMIC LEARNING THROUGH A REGRESSION-BASED LIBRARY GENERATION PROCESS,filed on Aug. 6, 2001, which is incorporated herein by reference in itsentirety.

4. Algorithm for Determining Simulated Diffraction Signal

As described above, simulated diffraction signals are generated to becompared to measured diffraction signals. As will be described below, inone exemplary embodiment, simulated diffraction signals can be generatedby applying Maxwell's equations and using a numerical analysis techniqueto solve Maxwell's equations. More particularly, in the exemplaryembodiment described below, rigorous coupled-wave analysis (RCWA) isused. It should be noted, however, that various numerical analysistechniques, including variations of RCWA, modal analysis, integralmethod, Green's functions, Fresnel method, finite element and the likecan be used.

In general, RCWA involves dividing a profile into a number of sections,slices, or slabs (hereafter simply referred to as sections). For eachsection of the profile, a system of coupled differential equationsgenerated using a Fourier expansion of Maxwell's equations (i.e., thecomponents of the electromagnetic field and permittivity (ε)). Thesystem of differential equations is then solved using a diagonalizationprocedure that involves eigenvalue and eigenvector decomposition (i.e.,Eigen-decomposition) of the characteristic matrix of the relateddifferential equation system. Finally, the solutions for each section ofthe profile are coupled using a recursive-coupling schema, such as ascattering matrix approach. For a description of a scattering matrixapproach, see Lifeng Li, “Formulation and comparison of two recursivematrix algorithms for modeling layered diffraction gratings,” J. Opt.Soc. Am. A13, pp 1024-1035 (1996), which is incorporated herein byreference in its entirety. Specifically for a more detail description ofRCWA, see U.S. patent application Ser. No. 09/770,997, titled CACHING OFINTRA-LAYER CALCULATIONS FOR RAPID RIGOROUS COUPLED-WAVE ANALYSES, filedon Jan. 25, 2001, which is incorporated herein by reference in itsentirety.

5. Machine Learning Systems

In one exemplary embodiment, simulated diffraction signals can begenerated using a machine learning system (MLS) employing a machinelearning algorithm, such as back-propagation, radial basis function,support vector, kernel regression, and the like. For a more detaileddescription of machine learning systems and algorithms, see “NeuralNetworks” by Simon Haykin, Prentice Hall, 1999, which is incorporatedherein by reference in its entirety. See also U.S. patent applicationSer. No. 10/608,300, titled OPTICAL METROLOGY OF STRUCTURES FORMED ONSEMICONDUCTOR WAFERS USING MACHINE LEARNING SYSTEMS, filed on Jun. 27,2003, which is incorporated herein by reference in its entirety.

6. Optimizing an Optical Metrology Model

With reference to FIG. 3, an exemplary process is depicted foroptimizing an optical metrology model. In step 300, an optical metrologymodel for a semiconductor structure is developed. An optical metrologymodel is typically expressed as a function of optical metrologyvariables related to the metrology device, materials and layers of thestructure, profile model of the structure, the simulation technique, andthe like. Optical metrology variables related to the metrology deviceinclude angle of incidence of the incident beam, beam intensity,wavelength calibration error, polarization dependent loss, spectrometerresolution, azimuth angle, numerical aperture, and the like. Opticalmetrology variables related to the materials and layers of the structureinclude the refractive indices (n), extinction coefficients (k), and thelike. Optical metrology variables related to the profile model includethe thickness of each of the underlying layers, the width of thestructure at various points such as the bottom, the middle or the top,the sidewall angle, pitch, and the like. Optical metrology variablesrelated to the simulation technique include wavelengths analyzed,resolution, diffraction orders, diffraction simulation algorithm, andthe like. For an exemplary method of developing an optical metrologymodel, refer to the University of California at Berkeley Doctoral Thesisof Xinhui Niu, “An Integrated System of Optical Metrology for DeepSub-Micron Lithography,” Apr. 20, 1999, the entire content of which isincorporated herein by reference.

In step 302, one or more goals for the optical modeling optimization areselected. In general, a goal for optimization is a performance measureof the effectiveness of the process of optimization. More particularly,with regards to optical modeling, optimization goals are yardsticks usedto measure the effectiveness of optimizing the optical metrology model.Examples of goals include accuracy of the measurement of the waferstructure, closeness of match of measurements made with differentmetrology systems, correlation coefficient, precision of themeasurement, throughput, CD uniformity, goodness-of-fit, cost function,and the like.

Accuracy typically refers to how well the measured value matches thetrue value of the structure being measured. Since the true value of thestructure being measured is generally not known, accuracy is typicallyexpressed as the absolute value of the difference between the measuredvalue using a metrology system and the measured value using acalibration standard. CDSEMs are typically calibrated using a pitchstandard wherein the pitch of a line and space grating is certified. Forscatterometry, the accuracy of a scatterometric measurement is evaluatedby comparing the scatterometric measurement using a metrology device toa reference metrology measurement, most typically a CDSEM measurement.However, other reference metrology measurements using XSEM, CD-AFM, TEM,and the like may also be used.

Closeness of match of measurements made with different metrology systemsas a goal may be expressed as an absolute measurement difference,average correlation ratio between a metrology system to a referencemetrology system, standard mean deviation (σ), multiples of standardmean deviation, (such as 2σ, 3σ, or 4σ), total measurement uncertainty(TMU), and the like. Absolute measurement difference is the absolutevalue of the difference between measurements of the same structure usingtwo different metrology devices, calculated for one measurement oraveraged over many measurements. The correlation of the measurements ofa metrology device (a first metrology device) compared to measurementsmade with a reference metrology device (a second metrology device) maybe expressed as TMU or correlation coefficient r. Used as goals, TMU maybe expressed in terms of three performance measures: offset average, theslope β, and TMU. The definitions and derivations of these performancemeasures are described in the Mandel method with the pertinent equationsshown below: $\begin{matrix}{\beta = \frac{S_{yy} - {\lambda\quad S_{XX}} + \sqrt{\left( {S_{YY} - {\lambda\quad S_{XX}}} \right)^{2}} + {4\quad\lambda\quad S_{YX}^{2}}}{2S_{YX}}} & (4.10) \\{{\hat{\sigma}}_{MANDEL} = \sqrt{\frac{\sum_{I = 1}^{N}\left\{ {\left( {y_{i} - {\hat{y}}_{i}} \right)^{2} + \left( {x_{i} - {\hat{x}}_{i}} \right)^{2}} \right\}}{N - 2}}} & (4.20) \\{{TMU} = {3\sqrt{{\hat{\sigma}}_{MANDEL}^{2} - {\hat{\sigma}}_{RT}^{2}}}} & (4.30) \\{{{Offset}\quad{average}} = \left( {\overset{\_}{x} - \overset{\_}{y}} \right)} & (4.40)\end{matrix}$where:$S_{YX} = {\sum_{i = 1}^{N}{\left( {y_{i} - {\overset{\_}{y}}_{i}} \right)\left( {x_{i} - \overset{\_}{x}} \right)}}$$S_{XX} = {\sum_{i = 1}^{N}\left( {x_{i} - \overset{\_}{x}} \right)^{2}}$

{circumflex over (σ)}_(RT) is the measurement uncertainty of the secondmetrology device,

{circumflex over (σ)}_(NT) is the measurement uncertainty of the firstmetrology device,

y represents values of measurement for the second metrology device,

x represents values of measurement for the first metrology device,$\lambda = \frac{\sigma_{RT}^{2}}{\sigma_{NT}^{2}}$is the Mandel parameter calculated as the ratio of the squaredmeasurement uncertainty of the second metrology device to the squaredmeasurement uncertainty of the first metrology device, and the caretsymbol, for example {circumflex over (σ)}_(NT), represents an estimateof the variable or quantity underneath it. For a discussion of theMandel method, refer to J. Mandel, “Fitting Straight Lines when BothVariables are Subject to Error”, Journal of Quality Technology, V1.16,No. 1, p.1-14, January 1984, which is incorporated herein in itsentirety.

Correlation coefficient r can be calculated using the formula:$\begin{matrix}{r = \frac{\sum\limits_{i}\quad{\left( {x_{i} - \overset{\_}{x}} \right)\left( {y_{i} - \overset{\_}{y}} \right)}}{\sqrt{\sum\limits_{i}\quad\left( {x_{i} - \overset{\_}{x}} \right)^{2}}\sqrt{\sum\limits_{i}\quad\left( {y_{i} - \overset{\_}{y}} \right)^{2}}}} & (2.60)\end{matrix}$where x_(i) and y_(i) are a pair of variables such as the measurementsmade using a reference metrology device and measurements of the newmetrology device, {overscore (x)} is the mean of x_(i)'s and {overscore(y)} is the mean of y_(i)'s. The value of r lies between −1 and +1inclusive. A correlation coefficient value of +1 can correspond tocomplete positive correlation and a value of −1 can correspond tocomplete negative correlation. A value of r close to zero can correspondto the x and y optimization parameters not being correlated.

Precision is an indication of how repeatable a measurement can be made.The ideal case is that of measuring a profile model parameter, such asbottom CD, ten times and the measurements yield the exact same answerevery time. Since there is always some amount of variation from onemeasurement to another, precision values are typically reported in termsof multiples of the standard deviation of the mean or the standarderror, sigma σ. Typically, a three-sigma, (3σ), where the valuesincluded in the stated range represent 99.7% of the data population. Itis understood that other statistical measures such as two-sigma,six-sigma and the like may be employed as well.

For optical metrology measurements, static precision, dynamic precision,and/or long term precision may be specified. Static precision refers tothe variation in measured value when no movement of the wafer relativeto the measurement optics occurs. Dynamic precision, also known asreproducibility, refers to the variation in measured value when thewafer is unloaded and reloaded between measurements. Long-term precisionrefers to the variation in measured value from lot to lot or over aperiod of time. In another embodiment, precision and accuracy ofmeasurement may be selected as goals for the metrology modeloptimization.

Throughput goals are typically expressed as number of wafers per hour.CD uniformity is typically expressed as a range of variation of a CDmeasurement across a wafer or across several wafers. For example, arange of 10 nm may be set as a goal for the bottom CD of a structureacross several selected sites in a wafer or across several wafersselected from a batch of wafers processed over a period of time.Goodness of fit and cost function are described in U.S. patentapplication Ser. No. 10/206,491 titled “Model And Parameter SelectionFor Optical Metrology”, filed on Jul. 25, 2002, which is incorporatedherein by reference in its entirety.

Goals may also involve aggregation of data, such as cost of ownership orreturn on investment. Cost of ownership related to metrology device maybe expressed in term of cost per wafer, cost per die, cost per endproduct, and the like. Return on investment may be expressed as numberof months or years needed to recoup the investment cost of a metrologydevice or pure percent return measured utilizing a discounted cash flowfinancial model.

Referring to FIG. 3, in step 304, one or more profile model parametersto be used in evaluating the one or more selected goals are selected.For example, assume the goal selected for metrology model optimizationis precision of measurement. Assume that the bottom CD of the waferstructure is the profile model parameter that is selected to be used inevaluating the selected goal. Thus, in this example, either staticand/or dynamic precision of the bottom CD is tracked when the opticalmetrology model is optimized. Evaluation of the goals is described in astep discussed below.

In step 306, one or more metrology model variables that are to be set tofixed values are selected. In step 308, the one or more selectedmetrology model variables are set to fixed values. For example, if themetrology device available for measurement is a single-wavelengthellipsometer, then the wavelength of the incident beam or beams can bethe metrology model variable that is selected and set to a fixed value,such as the wavelength values for the specific ellipsometer used. If themetrology device is a reflectometer, the angle of incidence of theincident beam can be the metrology model variable that is selected andset to a fixed value, such as zero degrees from normal. Other metrologymodel variables related to the metrology device that can be selected andset to fixed values include beam intensity, wavelength calibrationerror, polarization dependent loss, spectrometer resolution, azimuthangle, numerical aperture, and the like. Metrology model variablesrelated to the materials and layers of the structure that can beselected and set to fixed values include the refractive indices of thevarious underlying layers, the extinction coefficients of the variousunderlying layer, and the like. Metrology model variables related to theprofile model that can be selected and set to fixed values include thethickness of each of the underlying layers, the width of the structureat various points, such as the bottom, the middle or the top, thesidewall angle, pitch, and the like. Metrology model variables relatedto the simulation technique that can be selected and set to a fixedvalue include type of diffraction simulation algorithm used, thewavelengths or range of wavelengths analyzed, resolution, number oflayers to use in dividing up a hypothetical profile to generate asimulated diffraction signal, the number of harmonic orders to use ingenerating the set of simulated diffraction signals, and range of valuesof the profile model parameters may be set to fixed values.

Referring still to FIG. 3 in step 310, one or more termination criteriafor evaluating the goals of optimization are set. Acceptable ranges orlimit values of the goals may be used as termination criteria. If thegoals for optimization included a precision goal, then the acceptablerange of precision for the selected profile model parameters would beset. More specifically, if both static and dynamic precisions arespecified, then values of standard deviations of the mean for bothstatic and dynamic precisions may be set. For example, a 3σ value of0.20 nm and 0.65 nm for the bottom CD of a structure for static anddynamic precision respectively, the 3σ calculated after 50 measurementsof the structure may be set as the termination criteria for opticalmetrology optimization. Alternatively, precision and accuracy may beselected as goals for optimization. In this instance, the precision andaccuracy ranges or limit values may be set as the termination criteria.For example, an underlying oxide thickness precision of 0.06 nm and 0.10nm for static and dynamic precision respectively plus an accuracy of1.50 nm. As mentioned above, goodness of fit may also be selected as agoal alone or in conjunction with other goals. In this connection, agoodness of fit of 0.95 may be set as the termination criterion or inconjunction with static and dynamic precision.

In step 312, the optical metrology model is optimized. In general,optimization of an optical metrology model is performed to minimize ormaximize an objective function that depends on the set of opticalmetrology variables while satisfying selected constraints. If theobjective function is goodness of fit, the objective function ismaximized. If the objective function is an error metric, such as sumsquared error (SSE) between the simulated diffraction signal and themeasured diffraction signal, then the objective function is minimized.Other error metrics may be used, such as sum-squared-difference-logerror and other least square errors. The selected constraints of themetrology model optimization are the selected termination criteria.

Optimization may be performed using local or global optimization or acombination of global and local optimization. Examples of globaloptimization techniques include simulated annealing, genetic algorithms,tabu search, neural networks, branch-and-bound technique, and the like.Examples of local optimization include steepest descent, least squares,hill climber, and the like. Examples of combination global and localoptimization are simulated annealing combined with steepest descent andgenetic algorithm combined with a steepest descent or hill climber andthe like. Optimization of simulated-diffraction signals using rigorousmodels are discussed in University of California at Berkeley DoctoralThesis of Xinhui Niu, “An Integrated System of Optical Metrology forDeep Sub-Micron Lithography,” Apr. 20, 1999, the entire content of whichis incorporated herein by reference. Optimization for profile model andprofile model parameter selection, with the other metrology modelvariables, such as device, structure material, and simulation techniquevariables, set to fixed values, is described in U.S. patent applicationSer. No. 10/206,491 titled “Model And Parameter Selection For OpticalMetrology”, filed on Jul. 25, 2002, which is incorporated herein byreference in its entirety.

In step 314, measurements of the one or more profile model parametersselected in step 304 are obtained using the optimized optical metrologymodel. For example, assuming that the bottom CD was the profile modelparameter selected in step 304, bottom CD measurements are obtainedusing the optimized metrology model.

In step 316, a determination is made as to whether the one or moretermination criteria have been met using the measurements obtained instep 314. Using the example described above, assume that the terminationcriteria set in step 310 was a 3σ value of less than or equal to 0.20 nmstatic precision and a 3σ value of less than or equal to 0.65 nm dynamicprecision for the bottom CD of a structure. Further assume that 50static and dynamic measurements are needed to get a representativesample size to calculate the static and dynamic precision 3σ values.Thus, based on 50 diffraction measurements of the bottom CD determinedusing the optimized optical metrology model in step 314, the 3σ valuesfor static and dynamic precisions are calculated.

If the termination criteria are not met, in step 318, the selection ofmetrology model variables and/or the fixed values used to set theselected metrology model variables are revised, and then steps 312, 314,and 316 are iterated. In particular, some additional metrology modelvariables may be set to fixed values, some metrology model variablespreviously set to fixed values may be allowed to float, and/or the fixedvalues can be changed. For example, pitch may be allowed to float in afirst iteration, and then set to a fixed value in a subsequentiteration. The thickness of an underlying layer in a patterned structuremay be set to a fixed value in a first iteration, and then allowed tofloat in a subsequent iteration. The angle of incident radiation may bechanged from one fixed value in a first iteration to another fixed valuein a subsequent iteration.

In another embodiment, the one or more termination criteria may bechanged to satisfy the requirements of the application. For example,assume that a 3σ value was previously used as a termination criterion.The termination criterion may be changed to a 4σ or 6σ value due to newconsiderations. Alternatively, additional termination criteria may beadded. For example, in an expansion of the example above, a thirdtermination criteria of TMU less than or equal to 6 nm may be set. Forexample, the TMU may be calculated by comparing 50 scatterometrymeasurements of the bottom CD using the optimized optical metrologymodel versus fifty measurements of the same bottom CD using a referencemetrology device such as a CDSEM. Assume that the calculations of thestatic and dynamic precision values are the same as above. TMU iscalculated using the values of the bottom CD from the scatterometricmeasurement and the CDSEM measurement, utilizing equation 4.30 above. Ifthe value of the calculated 3σ static precision is less than or equal to0.20 nm, the calculated 3σ dynamic precision is less than or equal to0.65 nm, and the TMU is less than or equal to 6 nm, then the terminationcriteria are met. As mentioned above, the correlation coefficient r maybe used instead of or in conjunction with TMU. Other correlationformulas may be used, such as the multiple-correlation coefficient R,which takes into account correlation of a group of variables takensimultaneously. For a detailed discussion and derivation ofmultiple-correlation coefficient R, refer to Bevington, et al., “DataReduction and Error Analysis for the Physical Sciences”, Third Edition,pages 197-207, which is incorporated herein in its entirety.

With reference to FIG. 3, if the termination criteria are met in step316, the optimized optical metrology model is used to collectverification data in step 320. Verification data include profile modelparameter values determined using the optimized optical metrology model.Depending on the selected goal, verification data may also includeprofile model parameter values obtained from reference metrology devicemeasurements. Typically, the optimized optical metrology model is usedto create a library or a training data set to train an MLS. The libraryor the MLS-trained system is used to determine the profile modelparameters of the wafer structure from measurements of the diffractionsignal using a metrology device. By way of example, assume that theoptical metrology model for a given semiconductor application isoptimized with accuracy of the bottom CD selected as a goal. Assumefurther that the selected accuracy goal is the difference of the bottomCD determined using the library or MLS-trained system and the bottom CDobtained using a reference CDSEM. The verification data of bottom CDvalues is obtained by using the library or MLS-trained system createdusing the optimized optical metrology model.

In step 322, a determination is made as to whether the collectedverification data meets the metrology model goals within an acceptablerange. The metrology model goals are evaluated using the appropriateformulas discussed above, using the verification data obtained in step320. For example, if the metrology model goal is stated in terms of 3σstatic precision of the bottom CD, the standard deviation equation isused with the bottom CD measurements obtained in step 320. If thesemetrology model goals are not met, in step 324, the metrology modelgoals are analyzed for validity and revised as appropriate, or themetrology model variables of the optical metrology model are revised.Steps 304 to 322 are then iterated.

An example is described to highlight the iteration of processing. Assumethat a specific reflectometer is used for the particular application.Further, the optical metrology model uses the integral method forsimulating the diffraction signal. Assume that static/dynamic precisionand accuracy goals for measurement of the bottom CD were selected andset at less than or equal to 0.20, 0.30, and 3.0 nm, respectively.Assume that after the optical metrology model was optimized, thecalculated actual static/dynamic precision and accuracy from actualmeasurements of the bottom CD came out to be 0.20, 0.30, and 5.0 nm,respectively. It should be noted in this example that accuracy of thebottom CD measurement using the optimized optical metrology model failedthe accuracy criterion. Continuing with the example, assume that afteranalysis, the diffraction simulation technique was changed from theintegral method to RCWA. After the optical metrology model is changed toincorporate RCWA, steps 304 to 322 are iterated.

FIG. 4 is a block diagram of an exemplary system for optimizing anoptical metrology model based on goals. In one exemplary embodiment, amodel preprocessor 400 develops an optical metrology model. As describedabove, the optical metrology model has metrology model variables, whichincludes profile parameters of a profile model. A metrology modeloptimizer 430 optimizes the optical metrology model using fixed valuesfor one or more selected metrology model variables of the opticalmetrology model. A new metrology device 426 can be used to measure oneor more selected profile model parameters using the optimized opticalmetrology model. A comparator 408 can then determine if one or moretermination are met by the measurements obtained using new metrologydevice 426. As described above, the one or more termination criteriainclude one or more goals for metrology model optimization.

In one exemplary embodiment, model preprocessor 400 accepts andprocesses input data 450, which can include fabrication recipe data,metrology device(s) data, optimization goals, selected profile modelparameters, optimization termination criteria, values for variablesdetermined to be fixed for the application, and metrology modelassumptions. Model preprocessor 400 can develop an optical metrologymodel 466 based on input data 450, and transmit optical metrology model466 to metrology model optimizer 430. New metrology device 426 can beused to measure the wafer structure (not shown), and transmitmeasurements 464 to metrology model optimizer 430. New metrology device426 may be a reflectometer, ellipsometer, CDSEM, CD-AFM, XSEM, or thelike. In some applications, new metrology device 426 can include one ormore scatterometers, or one or more different types of optical metrologydevices. Metrology model optimizer 430 can invoke a global optimizer412, a local optimizer 416, or a combined global and local optimizer420. Global optimizer 412, local optimizer 416, or combined global andlocal optimizer 420 can perform the optimization of the opticalmetrology model as discussed above, using measurements 464 from newmetrology device 426 as input to extract the selected profile modelparameter values. The extracted selected profile model parameter valuesand optimization termination criteria 456 are transmitted to comparator408.

In the present exemplary embodiment, comparator 408 in FIG. 4 evaluatesthe goals using the profile model parameter values in transmitted data456, which includes comparing the calculated goals to the optimizationtermination criteria. If one of the goals is closeness of match ofmeasurements made with different metrology systems, then a referencemetrology device 424 can be used to measure the wafer structure and totransmit measurements 458 to comparator 408. Reference metrology device424 may be a reflectometer, ellipsometer, CDSEM, CD-AFM, XSEM, TEM, orthe like. Comparator 408 calculates the goals using transmitted data 456from metrology optimizer 430 and transmitted measurements 458, andcompares the calculated goals to the optimization termination criteriato test if the termination criteria are met. It is understood that basedon the goal, other input data 460, such cost of ownership, cash flowdata, and the like, may be transmitted to comparator 408 for calculationof the goal and later comparison of the calculated goal to theoptimization termination criteria.

Referring to FIG. 4, if the optimization termination criteria are met,then processing is complete. If the optimization termination criteriaare not met, the calculated values of goal 454 are transmitted to modeladjuster 404, which determines revisions to the selected modelvariables, revisions to the values used to set selected model variablesto fixed values, revisions to the termination criteria or otherrevisions to the assumptions of the optical metrology model. Revisions452 to the optical metrology model are transmitted to model preprocessor400 for another iteration of the metrology model optimization process.

It should be recognized that model preprocessor 400, metrology modeloptimizer 430, comparator 408, and model adjuster 404 can be embodied asany number of hardware, software, or combination of hardware andsoftware components or modules. Similarly, global optimizer 412, localoptimizer 416, and combined global and local optimizer 420 can beembodied as any number of hardware, software, or combination of hardwareand software components or modules.

FIG. 5 depicts an exemplary process for optimizing an optical metrologymodel for use in measuring a wafer structure. In step 510, an initialoptical metrology model is developed. In step 520, one or more goals formetrology model optimization are selected. As described above, the oneor more goals for metrology model optimization can include precision,accuracy, critical dimension uniformity, correlation coefficient,goodness-of-fit, cost function, figure of merit, throughput, closenessof match of measurements made with different metrology devices, cost ofownership, and/or return on investment.

In step 530, the initial optical metrology model is optimized to obtaina first optimized optical metrology model using a first metrology deviceassumption that includes one or more characteristics of a firstmetrology device that can be used to measure the wafer structure. Thefirst metrology device assumption may include specific data about thefirst metrology device from a specific manufacturer. For example, thefirst metrology device may be a broadband, non-polarized reflectometer.One of the characteristics of the first metrology device included in thefirst metrology device assumption can be that the angle of incidence ofthe incident beam used in the first metrology device is set to zerorelative to normal. If the first metrology device is assumed to be aspecific reflectometer from a specific manufacturer, then thecharacteristics of the first metrology device can include data relatedto beam intensity, wavelength calibration error, spectrometerresolution, azimuth angle, and numerical aperture. If an ellipsometer isassumed, then the characteristics of the first metrology device caninclude data related to the angle of incidence (AOI), AOI uncertainty,wavelength of incident beam, wavelength calibration error, spectrometerresolution, wavelength mixing, polarization dependent loss, phasecalibration error, and the like.

In step 540, the initial optical metrology model is optimized to obtainat least a second optimized optical metrology model using at least asecond metrology device assumption that includes one or morecharacteristics of at least a second metrology device that can be usedto measure the wafer structure. Similar to the first metrology deviceassumption, the second device assumption may include specific data aboutthe second metrology device from a specific manufacturer. Note, however,that the first and second metrology device assumptions are different. Inparticular, at least one characteristic of the first and at least secondmetrology devices is different. For example, the first metrology devicecan be assumed to be a reflectometer, and the second metrology devicecan be assumed to be an ellipsometer.

As indicated by the ellipsis after step 540, the initial opticalmetrology model can be optimized to obtain any number of additionaloptimized optical metrology models using any number of additionalmetrology device assumptions. For example, in step 550 of FIG. 5, theinitial optical metrology model can be optimized to obtain an n^(th)optimized optical metrology model using an n^(th) metrology deviceassumption.

In step 560, a preferred metrology device is selected based on thevarious optimized metrology models (i.e., the first optimized opticalmetrology model and the at least second optimized optical metrologymodel, which potentially includes the n^(th) optimized optical metrologymodel) and the one or more goals selected in step 520. In particular, inone exemplary embodiment, the various optimized optical metrology modelsare used to obtain measurements of the wafer structure using thecorresponding metrology devices (i.e., the first metrology device andthe at least second metrology device, which potentially includes then^(th) metrology device). The one or more goals selected in step 520 areevaluated using the obtained measurements. The preferred metrologydevice selected in step 560 has characteristics that are most similar tothe metrology device assumption of the optical metrology model thatproduced the measurements that best meets the one or more goals selectedin step 520.

For example, assume the one or more goals selected in step 520 includedstatic and dynamic precision, which are set at 0.02 nm and 0.06 nm,respectively. Assume that three optimized optical metrology models areobtained using three metrology device assumptions. In particular, assumethat three metrology device assumptions included assuming that the threemetrology devices were a regular non-polarized reflectometer, apolarized reflectometer, and an ellipsometer. Now assume that the staticand dynamic precision produced by using the optimized optical metrologymodel corresponding to the metrology device assumption that themetrology device is a polarized reflectometer is 0.015 nm and 0.045 nm,respectively, and best meets the static and dynamic precision goal of0.02 nm and 0.06 nm, respectively. Thus, in this example, a polarizedreflectometer is selected as the preferred metrology device.

FIG. 6 depicts an exemplary process for optimizing an optical metrologymodel for use in measuring a wafer structure. In step 610, an opticalmetrology model is developed. In step 620, one or more goals formetrology model optimization are selected. The one or more goals formetrology model optimization can include precision, accuracy, criticaldimension uniformity, correlation coefficient, goodness-of-fit, costfunction, figure of merit, throughput, closeness of match ofmeasurements made with different metrology devices, cost of ownership,and/or return on investment.

In step 630, one or more metrology model variables of the opticalmetrology model related to metrology devices are selected to be set tofixed values. As mentioned above, metrology model variables related tometrology devices can include wavelength of the incident beam, AOI, beamintensity, wavelength calibration error, polarization dependent loss,spectrometer resolution, azimuth angle, numerical aperture, and thelike. In step 640, the optical metrology model is optimized with theselected metrology model variables related to metrology devices allowedto float over a range of values. Note that the metrology model variablesrelated to metrology devices not selected in step 630 may be set tofixed values. The results of the optimization process can includeprofile model parameters and optimized values of the selected metrologymodel variables related to optical metrology devices. In step 650, ametrology device is selected based on the optimized values of theselected metrology device variables related to optical metrologydevices.

For example, assume that accuracy is the one or more goals selected instep 620, with the accuracy goal set at an absolute value of 3 nm.Further, assume the metrology model variables related to metrologydevices that are allowed to float over a range in the optimizationprocess include wavelength range, AOI, and azimuth angle. Now assumethat the optimized values for the metrology model variables related tometrology devices that were allowed to float during the optimizationprocess are a wavelength range of 300 to 820 nm, an AOI of 45 degreesfrom normal, and an azimuth angle of 5 degrees. Thus, in this example, ametrology device that can operate with a wavelength range of 300 to 820nm, an AOI of 45 degrees from normal, and an azimuth angle of 5 degreeswould be selected. If a variety of metrology devices were available in afabrication site, then the metrology device that meets the optimizedmetrology hardware variables would be selected for the specificsemiconductor application. Alternatively, metrology devices may have anoperating AOI, wavelength, and azimuth angle range. The values for AOI,wavelength, and azimuth angle from the optimization run would be used toset the metrology device for the given application.

Although exemplary embodiments have been described, variousmodifications can be made without departing from the spirit and/or scopeof the present invention. For example, a first iteration may be run witha high number of variables allowed to float. After the first iteration,variables that do not produce significant changes to the diffractionresponse may be set to fixed values. Alternatively, variables initiallyconsidered constant due to previous empirical data may be allowed tofloat after further analyses. Therefore, the present invention shouldnot be construed as being limited to the specific forms shown in thedrawings and described above but based on the claims below.

1. A method of evaluating optimization of an optical metrology model foruse in measuring a wafer structure, the method comprising: a) developingan optical metrology model, the optical metrology model having metrologymodel variables, which includes profile model parameters of a profilemodel; b) selecting one or more goals for metrology model optimization;c) selecting one or more profile model parameters to be used inevaluating the one or more selected goals; d) selecting one or moremetrology model variables to be set to fixed values; e) setting the oneor more selected metrology model variables to fixed values; f) settingone or more termination criteria for the one or more selected goals; g)optimizing the optical metrology model using the fixed values for theone or more selected metrology model variables; h) obtainingmeasurements for the one or more selected profile model parameters usingthe optimized optical metrology model; and i) determining if the one ormore termination criteria are met by the obtained measurements.
 2. Themethod of claim 1 wherein developing the optical metrology modelcomprises: specifying metrology model variables of the optical metrologymodel related to a fabrication recipe; specifying metrology modelvariables of the optical metrology model related to one or moremetrology devices; specifying metrology model variables of the opticalmetrology model related to simulation technique; specifying metrologymodel variables of the optical metrology model related to materials usedin the structure; and specifying metrology model variables of theoptical metrology model related to the profile model of the structure.3. The method of claim 2 wherein the metrology model variables of theoptical metrology model related to one or more metrology devices includewavelength of the incident beam or beams, the angle of incidence of theincident beam, beam intensity, wavelength calibration error,polarization dependent loss, spectrometer resolution, azimuth angle,and/or numerical aperture.
 4. The method of claim 2 wherein themetrology model variables of the optical metrology model related to thesimulation technique include wavelength analyzed, resolution,diffraction orders, and diffraction simulation algorithm.
 5. The methodof claim 4 wherein the diffraction simulation algorithm includesrigorous coupled-wave analysis (RCWA), modal analysis, integral method,Green's functions, Fresnel method, or finite element analysis.
 6. Themethod of claim 2 wherein the metrology model variables of the opticalmetrology model related to materials used in the structure includerefractive index and extinction coefficient.
 7. The method of claim 2wherein the metrology model variables of the optical metrology modelrelated to the profile model of the structure include thickness ofunderlying films, pitch, width of structure at various heights, heightof structure, sidewall angle, and/or footing.
 8. The method of claim 1wherein the one or more goals for metrology model optimization selectedin b) include precision, accuracy, critical dimension uniformity,correlation coefficient, goodness-of-fit, cost function, figure ofmerit, throughput, closeness of match of measurements made withdifferent metrology devices, cost of ownership, and/or return oninvestment.
 9. The method of claim 8 wherein precision includes static,dynamic, and/or long term precision.
 10. The method of claim 8 whereincloseness of match of measurements made with different metrology devicesincludes absolute measurement difference, average correlation ratiobetween a metrology system to a reference metrology system, standardmean deviation (σ), multiples of standard mean deviation, and/or totalmeasurement uncertainty.
 11. The method of claim 1 wherein the one ormore goals selected in b) include static and dynamic precision ofmeasurement, and wherein the one or more profile model parametersselected in c) include a bottom width of the structure.
 12. The methodof claim 1 wherein the one or more goals selected in b) include accuracyof measurement, and wherein the one or more profile model parametersselected in c) include a middle critical dimension of the structure. 13.The method of claim 1 wherein the one or more goals selected in b)include total measurement uncertainty (TMU) and/or critical dimensionuniformity, and wherein the one or more profile model parametersselected in c) include a middle critical dimension of the structure. 14.The method of claim 1 wherein the one or more metrology model variablesselected in d) include metrology model variables related to metrologydevices.
 15. The method of claim 1 wherein the one or more metrologymodel variables selected in d) include metrology model variables relatedto materials of the structure.
 16. The method of claim 1 wherein the oneor more metrology model variables selected in d) include metrology modelvariables related to the profile model of the structure.
 17. The methodof claim 1 wherein the one or more termination criteria set in f)include ranges of precision, accuracy, critical dimension uniformity,correlation coefficient, goodness-of-fit, cost function, throughput,closeness of match of measurements made with different metrologydevices, cost of ownership, and/or return on investment.
 18. The methodof claim 1 wherein the one or more termination criteria set in f)include a range of total measurement uncertainty of measurements madewith a new metrology device compared to a reference metrology device.19. The method of claim 1 wherein optimization of the optical metrologymodel performed in g) includes utilizing global optimization techniques.20. The method of claim 19 wherein the global optimization techniqueincludes simulated annealing, genetic algorithms, tabu search, neuralnetworks, or branch-and-bound technique.
 21. The method of claim 1wherein optimization of the optical metrology model performed in g)includes utilizing local optimization techniques.
 22. The method ofclaim 1 wherein optimization of the optical metrology model performed ing) includes utilizing a combination of global and local optimizationtechniques.
 23. The method of claim 22 wherein optimization of theoptical metrology model performed in g) includes utilizing a combinationof genetic algorithms and a local optimization technique.
 24. The methodof claim 22 wherein optimization of the optical metrology modelperformed in g) includes utilizing a combination of simulated annealingand a local optimization technique.
 25. The method of claim 1 wherein ifthe one or more termination criteria are not met in i): j) revising theselection of metrology model variables in d) and/or the fixed values setto the selected metrology model variables in e); and k) iterating stepsg), h), and i).
 26. The method of claim 25 wherein revising theselection of metrology model variables comprises allowing one or moremetrology model variables to float instead of being set to fixed values.27. The method of claim 25 wherein revising selection of metrology modelvariables comprises setting one or more metrology model variables tofixed values instead of being allowed to float.
 28. The method of claim1 wherein if the one or more termination criteria are not met in i):changing the termination criteria; or adding additional terminationcriteria.
 29. The method of claim 1 wherein if the one or moretermination criteria are met in i): collecting verification data usingthe optimized optical metrology model; and determining if the collectedverification data meet the one or more selected goals within acceptableranges.
 30. The method of claim 29 wherein if the collected verificationdata do not meet the one or more selected goals within acceptableranges: revising the selection of the one or more metrology model goalsin b); or revising one or more of metrology model variables of theoptical metrology model.
 31. The method of claim 30 wherein revising oneor more metrology model variables of the optical metrology model includechanging diffraction simulation technique to rigorous coupled-waveanalysis (RCWA), modal analysis, integral method, Green's functions,Fresnel method, or finite element analysis.
 32. A method of evaluatingoptimization of an optical metrology model for use in measuring a waferstructure, the method comprising: a) developing an initial opticalmetrology model; b) selecting one or more goals for metrology modeloptimization; c) optimizing the initial optical metrology model toobtain a first optimized optical metrology model using a first metrologydevice assumption that includes one or more characteristics of a firstmetrology device that can be used to measure the wafer structure; d)optimizing the initial optical metrology model to obtain at least asecond optimized optical metrology model using a second metrology deviceassumption that includes one or more characteristics of a secondmetrology device that can be used to measure the wafer structure,wherein the first and second metrology device assumptions differ; and e)selecting a preferred metrology device based on the first and at leastsecond optimized optical metrology models and the one or more selectedgoals.
 33. The method of claim 32 wherein e) comprises: obtainingmeasurements of the wafer structure using the first and at least secondoptimized optical metrology models; evaluating the one or more selectedgoals using the obtained measurements; when the obtained measurementsusing the first optimized optical metrology model better meets the oneor more selected goals, selecting as the preferred metrology device ametrology device with characteristics that are most similar to the firstmetrology device; and when the obtained measurements using the at leastsecond optimized optical metrology model better meets the one or moreselected goals, selecting as the preferred metrology device a metrologydevice with characteristics that are most similar to the secondmetrology device.
 34. The method of claim 33 wherein the measurements ofthe wafer structure are obtained using the first and at least secondoptical metrology devices.
 35. The method of claim 32 wherein thecharacteristics of the first or second metrology device include angle ofincidence of an incident beam.
 36. The method of claim 32 wherein thecharacteristics of the first or second metrology device include beamintensity, wavelength calibration error, spectrometer resolution,azimuth angle, and numerical aperture.
 37. The method of claim 32wherein the first metrology device is a reflectometer, and wherein thesecond metrology device is an ellipsometer.
 38. The method of claim 32wherein the one or more goals for metrology model optimization selectedin b) include precision, accuracy, critical dimension uniformity,correlation coefficient, goodness-of-fit, cost function, figure ofmerit, throughput, closeness of match of measurements made withdifferent metrology devices, cost of ownership, and/or return oninvestment.
 39. A method of evaluating optimization of an opticalmetrology model for use in measuring a wafer structure, the methodcomprising: a) developing an optical metrology model having one or moreoptical metrology variables related to metrology devices; b) selectingone or more goals for metrology model optimization; c) selecting one ormore of the metrology model variables related to optical metrologydevices; d) optimizing the optical metrology model with the one or moreselected metrology model variables related to optical metrology devicesallowed to float over a range of values; and e) selecting a metrologydevice based on optimized values of the one or more selected metrologymodel variables obtained from the optimized optical metrology model. 40.The method of claim 39 wherein the one or more selected metrology modelvariables related to optical metrology devices include angle ofincidence, azimuth angle, and/or wavelength.
 41. The method of claim 39wherein the one or more goals for metrology model optimization selectedin b) include precision, accuracy, critical dimension uniformity,correlation coefficient, goodness-of-fit, cost function, figure ofmerit, throughput, closeness of match of measurements made withdifferent metrology devices, cost of ownership, and/or return oninvestment.
 42. A system for evaluating optimization of an opticalmetrology model for use in measuring wafer structures, the systemcomprising: a model preprocessor configured to develop an opticalmetrology model having metrology model variables, which includes profileparameters of a profile model; a metrology model optimizer configured tooptimize the optical metrology model using fixed values for one or moreselected metrology model variables of the optical metrology model; afirst metrology device configured to measure one or more selectedprofile model parameters using the optimized optical metrology model;and a comparator configured to determine if one or more terminationcriteria are met by the measurements obtained using the first metrologydevice, wherein the one or more termination criteria include one or moregoals for metrology model optimization.
 43. The system of claim 42further comprising: a model adjuster configured to receive data from thecomparator, to revise the one or more selected metrology modelvariables, and to revise fixed values for the one or more selectedmetrology model variables.
 44. The system of claim 42 wherein the modelpreprocessor is configured to process input data, including fabricationrecipe data, metrology device data, optimization goals, selected profilemodel parameters, optimization termination criteria, fixed values formetrology model variables.
 45. The system of claim 44 wherein themetrology model optimizer is configured to receive transmitted data fromthe model preprocessor and the first metrology device, to invoke aglobal optimizer, a local optimizer, or a combined global and localoptimizer, and to extract values of the selected profile model parametermeasurements from the first metrology device, and to transmit theextracted values and optimization termination criteria.
 46. The systemof claim 45, wherein the comparator is configured to receive the valuesand optimization termination criteria, and to evaluate the optimizationgoals based on the received values.
 47. The system of claim 42 whereinthe first metrology device is a scatterometric device.
 48. The system ofclaim 47 wherein the scatterometric device is a reflectometer orellipsometer.
 49. The system of claim 42 wherein the metrology optimizerincludes a global optimizer, which includes a simulated annealing,genetic algorithm, tabu search, neural network, or branch-and-boundtechnique.
 50. The system of claim 42 wherein the metrology optimizerincludes a local optimizer, which includes steepest descent or hillclimber.
 51. The system of claim 42 further comprising a secondmetrology device configured to measure the one or more selected profilemodel parameters.
 52. The system of claim 51 wherein the secondmetrology device is a critical dimension scanning electron microscope, acritical dimension atomic force microscope, a cross section scanningelectron microscope, a transmission electron microscope, or ascatterometer.
 53. A computer-readable storage medium containingcomputer executable instructions for causing a computer to optimize anoptical metrology model for use in measuring a wafer structure,comprising instructions for: a) developing an optical metrology model,the optical metrology model having metrology model variables, whichincludes profile model parameters of a profile model; b) selecting oneor more goals for metrology model optimization; c) selecting one or moreprofile model parameters to be used in evaluating the one or moreselected goals; d) selecting one or more metrology model variables to beset to fixed values; e) setting the one or more selected metrology modelvariables to fixed values; f) setting one or more termination criteriafor the one or more selected goals; g) optimizing the optical metrologymodel using the fixed values for the one or more selected metrologymodel variables; h) obtaining measurements for the one or more selectedprofile model parameters using the optimized optical metrology model;and i) determining if the one or more termination criteria are met bythe obtained measurements.
 54. A computer-readable storage mediumcontaining computer executable instructions for causing a computer tooptimize an optical metrology model for use in measuring a waferstructure, comprising instructions for: a) developing an initial opticalmetrology model; b) selecting one or more goals for metrology modeloptimization; c) optimizing the initial optical metrology model toobtain a first optimized optical metrology model using a first metrologydevice assumption that includes one or more characteristics of a firstmetrology device that can be used to measure the wafer structure; d)optimizing the initial optical metrology model to obtain at least asecond optimized optical metrology model using a second metrology deviceassumption that includes one or more characteristics of a secondmetrology device that can be used to measure the wafer structure,wherein the first and second metrology device assumptions differ; and e)selecting a preferred metrology device based on the first and at leastsecond optimized optical metrology models and the one or more selectedgoals.
 55. A computer-readable storage medium containing computerexecutable instructions for causing a computer to optimize an opticalmetrology model for use in measuring a wafer structure, comprisinginstructions for: a) developing an optical metrology model having one ormore optical metrology variables related to metrology devices; b)selecting one or more goals for metrology model optimization; c)selecting one or more of the metrology model variables related tooptical metrology devices; d) optimizing the optical metrology modelwith the one or more selected metrology model variables related tooptical metrology devices allowed to float over a range of values; ande) selecting a metrology device based on optimized values of the one ormore selected metrology model variables obtained from the optimizedoptical metrology model.